Overview

Dataset statistics

Number of variables34
Number of observations76
Missing cells23
Missing cells (%)0.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory20.3 KiB
Average record size in memory273.7 B

Variable types

Categorical2
Numeric32

Alerts

Province/State has a high cardinality: 53 distinct values High cardinality
Long is highly correlated with 1/23/20 and 28 other fieldsHigh correlation
1/22/20 is highly correlated with 1/23/20 and 28 other fieldsHigh correlation
1/23/20 is highly correlated with Long and 29 other fieldsHigh correlation
1/24/20 is highly correlated with Long and 29 other fieldsHigh correlation
1/25/20 is highly correlated with Long and 29 other fieldsHigh correlation
1/26/20 is highly correlated with Long and 29 other fieldsHigh correlation
1/27/20 is highly correlated with Long and 29 other fieldsHigh correlation
1/28/20 is highly correlated with Long and 29 other fieldsHigh correlation
1/29/20 is highly correlated with Long and 29 other fieldsHigh correlation
1/30/20 is highly correlated with Long and 29 other fieldsHigh correlation
1/31/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/1/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/2/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/3/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/4/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/5/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/6/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/7/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/8/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/9/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/10/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/11/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/12/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/13/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/14/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/15/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/16/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/17/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/18/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/19/20 is highly correlated with Long and 29 other fieldsHigh correlation
2/20/20 is highly correlated with Long and 29 other fieldsHigh correlation
1/22/20 is highly correlated with 1/23/20 and 28 other fieldsHigh correlation
1/23/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/24/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/25/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/26/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/27/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/28/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/29/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/30/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/31/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/1/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/2/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/3/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/4/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/5/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/6/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/7/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/8/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/9/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/10/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/11/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/12/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/13/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/14/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/15/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/16/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/17/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/18/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/19/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/20/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/22/20 is highly correlated with 1/23/20 and 28 other fieldsHigh correlation
1/23/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/24/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/25/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/26/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/27/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/28/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/29/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/30/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
1/31/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/1/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/2/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/3/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/4/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/5/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/6/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/7/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/8/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/9/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/10/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/11/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/12/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/13/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/14/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/15/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/16/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/17/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/18/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/19/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
2/20/20 is highly correlated with 1/22/20 and 28 other fieldsHigh correlation
Country/Region is highly correlated with Province/StateHigh correlation
Province/State is highly correlated with Country/RegionHigh correlation
Province/State is highly correlated with Country/Region and 32 other fieldsHigh correlation
Country/Region is highly correlated with Province/State and 2 other fieldsHigh correlation
Lat is highly correlated with Province/State and 2 other fieldsHigh correlation
Long is highly correlated with Province/State and 2 other fieldsHigh correlation
1/22/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
1/23/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
1/24/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
1/25/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
1/26/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
1/27/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
1/28/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
1/29/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
1/30/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
1/31/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/1/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/2/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/3/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/4/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/5/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/6/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/7/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/8/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/9/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/10/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/11/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/12/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/13/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/14/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/15/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/16/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/17/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/18/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/19/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
2/20/20 is highly correlated with Province/State and 29 other fieldsHigh correlation
Province/State has 23 (30.3%) missing values Missing
Province/State is uniformly distributed Uniform
Lat has unique values Unique
Long has unique values Unique
1/22/20 has 47 (61.8%) zeros Zeros
1/23/20 has 39 (51.3%) zeros Zeros
1/24/20 has 36 (47.4%) zeros Zeros
1/25/20 has 33 (43.4%) zeros Zeros
1/26/20 has 27 (35.5%) zeros Zeros
1/27/20 has 24 (31.6%) zeros Zeros
1/28/20 has 23 (30.3%) zeros Zeros
1/29/20 has 20 (26.3%) zeros Zeros
1/30/20 has 17 (22.4%) zeros Zeros
1/31/20 has 11 (14.5%) zeros Zeros
2/1/20 has 8 (10.5%) zeros Zeros
2/2/20 has 8 (10.5%) zeros Zeros
2/3/20 has 7 (9.2%) zeros Zeros
2/4/20 has 6 (7.9%) zeros Zeros
2/5/20 has 5 (6.6%) zeros Zeros
2/6/20 has 5 (6.6%) zeros Zeros
2/7/20 has 4 (5.3%) zeros Zeros
2/8/20 has 4 (5.3%) zeros Zeros
2/9/20 has 4 (5.3%) zeros Zeros
2/10/20 has 4 (5.3%) zeros Zeros
2/11/20 has 3 (3.9%) zeros Zeros
2/12/20 has 3 (3.9%) zeros Zeros
2/13/20 has 2 (2.6%) zeros Zeros
2/14/20 has 1 (1.3%) zeros Zeros
2/15/20 has 1 (1.3%) zeros Zeros
2/16/20 has 1 (1.3%) zeros Zeros
2/17/20 has 1 (1.3%) zeros Zeros
2/18/20 has 1 (1.3%) zeros Zeros

Reproduction

Analysis started2022-12-14 06:41:16.069116
Analysis finished2022-12-14 06:42:59.864055
Duration1 minute and 43.79 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

Province/State
Categorical

HIGH CARDINALITY
HIGH CORRELATION
HIGH CORRELATION
MISSING
UNIFORM

Distinct53
Distinct (%)100.0%
Missing23
Missing (%)30.3%
Memory size736.0 B
Anhui
 
1
Orange, CA
 
1
Yunnan
 
1
Zhejiang
 
1
Taiwan
 
1
Other values (48)
48 

Length

Max length28
Median length8
Mean length9.283018868
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)100.0%

Sample

1st rowAnhui
2nd rowBeijing
3rd rowChongqing
4th rowFujian
5th rowGansu

Common Values

ValueCountFrequency (%)
Anhui1
 
1.3%
Orange, CA1
 
1.3%
Yunnan1
 
1.3%
Zhejiang1
 
1.3%
Taiwan1
 
1.3%
Seattle, WA1
 
1.3%
Chicago, IL1
 
1.3%
Tempe, AZ1
 
1.3%
Macau1
 
1.3%
Hong Kong1
 
1.3%
Other values (43)43
56.6%
(Missing)23
30.3%

Length

2022-12-14T00:42:59.955291image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ca5
 
6.2%
san3
 
3.7%
south2
 
2.5%
on2
 
2.5%
anhui1
 
1.2%
fujian1
 
1.2%
hainan1
 
1.2%
guizhou1
 
1.2%
guangxi1
 
1.2%
guangdong1
 
1.2%
Other values (63)63
77.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Country/Region
Categorical

HIGH CORRELATION
HIGH CORRELATION

Distinct31
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Memory size736.0 B
Mainland China
31 
US
11 
Australia
Canada
 
3
UK
 
1
Other values (26)
26 

Length

Max length20
Median length9
Mean length9.236842105
Min length2

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)35.5%

Sample

1st rowMainland China
2nd rowMainland China
3rd rowMainland China
4th rowMainland China
5th rowMainland China

Common Values

ValueCountFrequency (%)
Mainland China31
40.8%
US11
 
14.5%
Australia4
 
5.3%
Canada3
 
3.9%
UK1
 
1.3%
United Arab Emirates1
 
1.3%
Philippines1
 
1.3%
India1
 
1.3%
Italy1
 
1.3%
Russia1
 
1.3%
Other values (21)21
27.6%

Length

2022-12-14T00:43:00.063699image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mainland31
27.7%
china31
27.7%
us11
 
9.8%
australia4
 
3.6%
canada3
 
2.7%
singapore1
 
0.9%
thailand1
 
0.9%
malaysia1
 
0.9%
nepal1
 
0.9%
france1
 
0.9%
Other values (27)27
24.1%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Lat
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.25016803
Minimum-37.8136
Maximum61.9241
Zeros0
Zeros (%)0.0%
Negative4
Negative (%)5.3%
Memory size736.0 B
2022-12-14T00:43:00.185404image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-37.8136
5-th percentile-5.853375
Q124.6878525
median33.83496
Q340.7547775
95-th percentile52.2188
Maximum61.9241
Range99.7377
Interquartile range (IQR)16.066925

Descriptive statistics

Standard deviation19.27479329
Coefficient of variation (CV)0.6371797101
Kurtosis4.559374318
Mean30.25016803
Median Absolute Deviation (MAD)7.607505
Skewness-1.822047237
Sum2299.01277
Variance371.5176565
MonotonicityNot monotonic
2022-12-14T00:43:00.323806image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.825711
 
1.3%
34.05221
 
1.3%
61.92411
 
1.3%
51.16571
 
1.3%
7.87311
 
1.3%
12.56571
 
1.3%
-27.46981
 
1.3%
-37.81361
 
1.3%
-33.86881
 
1.3%
33.78791
 
1.3%
Other values (66)66
86.8%
ValueCountFrequency (%)
-37.81361
1.3%
-34.92851
1.3%
-33.86881
1.3%
-27.46981
1.3%
1.35211
1.3%
4.21051
1.3%
7.87311
1.3%
12.56571
1.3%
12.87971
1.3%
13.75631
1.3%
ValueCountFrequency (%)
61.92411
1.3%
61.5241
1.3%
60.12821
1.3%
55.37811
1.3%
51.16571
1.3%
50.50391
1.3%
49.28271
1.3%
47.8621
1.3%
47.75111
1.3%
46.22761
1.3%

Long
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct76
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.66383105
Minimum-123.121
Maximum153.0251
Zeros0
Zeros (%)0.0%
Negative16
Negative (%)21.1%
Memory size736.0 B
2022-12-14T00:43:00.452136image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-123.121
5-th percentile-118.867775
Q112.038425
median105.1549
Q3116.61725
95-th percentile139.641075
Maximum153.0251
Range276.1461
Interquartile range (IQR)104.578825

Descriptive statistics

Standard deviation86.7537093
Coefficient of variation (CV)1.454041884
Kurtosis-0.1737971316
Mean59.66383105
Median Absolute Deviation (MAD)18.71519
Skewness-1.153727031
Sum4534.45116
Variance7526.206077
MonotonicityNot monotonic
2022-12-14T00:43:00.570624image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117.22641
 
1.3%
-118.24371
 
1.3%
25.74821
 
1.3%
10.45151
 
1.3%
80.77181
 
1.3%
104.9911
 
1.3%
153.02511
 
1.3%
144.96311
 
1.3%
151.20931
 
1.3%
-117.85311
 
1.3%
Other values (66)66
86.8%
ValueCountFrequency (%)
-123.1211
1.3%
-121.95521
1.3%
-120.98761
1.3%
-120.741
1.3%
-118.24371
1.3%
-117.85311
1.3%
-117.16111
1.3%
-111.0941
1.3%
-98.49361
1.3%
-89.40121
1.3%
ValueCountFrequency (%)
153.02511
1.3%
151.20931
1.3%
144.96311
1.3%
139.65031
1.3%
139.6381
1.3%
138.60071
1.3%
127.76221
1.3%
126.9781
1.3%
126.19171
1.3%
122.60861
1.3%

1/22/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.302631579
Minimum0
Maximum444
Zeros47
Zeros (%)61.8%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:00.678452image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile9.25
Maximum444
Range444
Interquartile range (IQR)1

Descriptive statistics

Standard deviation50.89938303
Coefficient of variation (CV)6.970005604
Kurtosis75.14052254
Mean7.302631579
Median Absolute Deviation (MAD)0
Skewness8.646574096
Sum555
Variance2590.747193
MonotonicityNot monotonic
2022-12-14T00:43:00.761690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
047
61.8%
112
 
15.8%
26
 
7.9%
43
 
3.9%
52
 
2.6%
141
 
1.3%
61
 
1.3%
261
 
1.3%
4441
 
1.3%
91
 
1.3%
ValueCountFrequency (%)
047
61.8%
112
 
15.8%
26
 
7.9%
43
 
3.9%
52
 
2.6%
61
 
1.3%
91
 
1.3%
101
 
1.3%
141
 
1.3%
261
 
1.3%
ValueCountFrequency (%)
4441
 
1.3%
261
 
1.3%
141
 
1.3%
101
 
1.3%
91
 
1.3%
61
 
1.3%
52
 
2.6%
43
 
3.9%
26
7.9%
112
15.8%

1/23/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct15
Distinct (%)19.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.592105263
Minimum0
Maximum444
Zeros39
Zeros (%)51.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:00.852205image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q33
95-th percentile17.5
Maximum444
Range444
Interquartile range (IQR)3

Descriptive statistics

Standard deviation50.94105813
Coefficient of variation (CV)5.928821467
Kurtosis73.96835343
Mean8.592105263
Median Absolute Deviation (MAD)0
Skewness8.549757362
Sum653
Variance2594.991404
MonotonicityNot monotonic
2022-12-14T00:43:00.948010image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
039
51.3%
19
 
11.8%
27
 
9.2%
55
 
6.6%
34
 
5.3%
93
 
3.9%
221
 
1.3%
321
 
1.3%
4441
 
1.3%
71
 
1.3%
Other values (5)5
 
6.6%
ValueCountFrequency (%)
039
51.3%
19
 
11.8%
27
 
9.2%
34
 
5.3%
41
 
1.3%
55
 
6.6%
61
 
1.3%
71
 
1.3%
81
 
1.3%
93
 
3.9%
ValueCountFrequency (%)
4441
 
1.3%
321
 
1.3%
271
 
1.3%
221
 
1.3%
161
 
1.3%
93
3.9%
81
 
1.3%
71
 
1.3%
61
 
1.3%
55
6.6%

1/24/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct19
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.38157895
Minimum0
Maximum549
Zeros36
Zeros (%)47.4%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:01.038147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q35
95-th percentile29.25
Maximum549
Range549
Interquartile range (IQR)5

Descriptive statistics

Standard deviation63.1942966
Coefficient of variation (CV)5.103896431
Kurtosis71.98063228
Mean12.38157895
Median Absolute Deviation (MAD)1
Skewness8.385933065
Sum941
Variance3993.519123
MonotonicityNot monotonic
2022-12-14T00:43:01.128428image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
036
47.4%
210
 
13.2%
14
 
5.3%
34
 
5.3%
153
 
3.9%
53
 
3.9%
82
 
2.6%
92
 
2.6%
42
 
2.6%
361
 
1.3%
Other values (9)9
 
11.8%
ValueCountFrequency (%)
036
47.4%
14
 
5.3%
210
 
13.2%
34
 
5.3%
42
 
2.6%
53
 
3.9%
82
 
2.6%
92
 
2.6%
101
 
1.3%
153
 
3.9%
ValueCountFrequency (%)
5491
 
1.3%
531
 
1.3%
431
 
1.3%
361
 
1.3%
271
 
1.3%
241
 
1.3%
231
 
1.3%
201
 
1.3%
181
 
1.3%
153
3.9%

1/25/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct28
Distinct (%)36.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.86842105
Minimum0
Maximum761
Zeros33
Zeros (%)43.4%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:01.231200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q310.25
95-th percentile46.5
Maximum761
Range761
Interquartile range (IQR)10.25

Descriptive statistics

Standard deviation87.70714028
Coefficient of variation (CV)4.64835611
Kurtosis70.92307229
Mean18.86842105
Median Absolute Deviation (MAD)2
Skewness8.297281292
Sum1434
Variance7692.542456
MonotonicityNot monotonic
2022-12-14T00:43:01.327800image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
033
43.4%
36
 
7.9%
14
 
5.3%
24
 
5.3%
183
 
3.9%
43
 
3.9%
72
 
2.6%
391
 
1.3%
621
 
1.3%
111
 
1.3%
Other values (18)18
23.7%
ValueCountFrequency (%)
033
43.4%
14
 
5.3%
24
 
5.3%
36
 
7.9%
43
 
3.9%
51
 
1.3%
61
 
1.3%
72
 
2.6%
81
 
1.3%
91
 
1.3%
ValueCountFrequency (%)
7611
1.3%
781
1.3%
621
1.3%
571
1.3%
431
1.3%
411
1.3%
391
1.3%
331
1.3%
321
1.3%
281
1.3%

1/26/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct29
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.86842105
Minimum0
Maximum1058
Zeros27
Zeros (%)35.5%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:01.430372image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q315.25
95-th percentile77
Maximum1058
Range1058
Interquartile range (IQR)15.25

Descriptive statistics

Standard deviation122.3272488
Coefficient of variation (CV)4.389457463
Kurtosis69.52288192
Mean27.86842105
Median Absolute Deviation (MAD)3
Skewness8.181224392
Sum2118
Variance14963.95579
MonotonicityNot monotonic
2022-12-14T00:43:01.530768image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
027
35.5%
19
 
11.8%
47
 
9.2%
33
 
3.9%
222
 
2.6%
82
 
2.6%
72
 
2.6%
362
 
2.6%
52
 
2.6%
441
 
1.3%
Other values (19)19
25.0%
ValueCountFrequency (%)
027
35.5%
19
 
11.8%
21
 
1.3%
33
 
3.9%
47
 
9.2%
52
 
2.6%
72
 
2.6%
82
 
2.6%
91
 
1.3%
131
 
1.3%
ValueCountFrequency (%)
10581
1.3%
1111
1.3%
1041
1.3%
831
1.3%
751
1.3%
691
1.3%
681
1.3%
601
1.3%
461
1.3%
441
1.3%

1/27/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct33
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.51315789
Minimum0
Maximum1423
Zeros24
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:01.628939image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q323.75
95-th percentile114.5
Maximum1423
Range1423
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation164.6552352
Coefficient of variation (CV)4.275298216
Kurtosis69.08540348
Mean38.51315789
Median Absolute Deviation (MAD)4
Skewness8.143944387
Sum2927
Variance27111.34649
MonotonicityNot monotonic
2022-12-14T00:43:01.730271image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
024
31.6%
111
14.5%
44
 
5.3%
53
 
3.9%
63
 
3.9%
82
 
2.6%
72
 
2.6%
1282
 
2.6%
261
 
1.3%
21
 
1.3%
Other values (23)23
30.3%
ValueCountFrequency (%)
024
31.6%
111
14.5%
21
 
1.3%
31
 
1.3%
44
 
5.3%
53
 
3.9%
63
 
3.9%
72
 
2.6%
82
 
2.6%
111
 
1.3%
ValueCountFrequency (%)
14231
1.3%
1511
1.3%
1282
2.6%
1101
1.3%
1001
1.3%
801
1.3%
751
1.3%
721
1.3%
701
1.3%
691
1.3%

1/28/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct35
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.39473684
Minimum0
Maximum3554
Zeros23
Zeros (%)30.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:01.838692image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q333.25
95-th percentile149.25
Maximum3554
Range3554
Interquartile range (IQR)33.25

Descriptive statistics

Standard deviation407.2512764
Coefficient of variation (CV)5.548780388
Kurtosis73.9422462
Mean73.39473684
Median Absolute Deviation (MAD)4
Skewness8.545749614
Sum5578
Variance165853.6021
MonotonicityNot monotonic
2022-12-14T00:43:01.943586image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
023
30.3%
111
14.5%
45
 
6.6%
73
 
3.9%
83
 
3.9%
332
 
2.6%
241
 
1.3%
951
 
1.3%
661
 
1.3%
271
 
1.3%
Other values (25)25
32.9%
ValueCountFrequency (%)
023
30.3%
111
14.5%
21
 
1.3%
45
 
6.6%
61
 
1.3%
73
 
3.9%
83
 
3.9%
91
 
1.3%
101
 
1.3%
111
 
1.3%
ValueCountFrequency (%)
35541
1.3%
2071
1.3%
1731
1.3%
1681
1.3%
1431
1.3%
1321
1.3%
1091
1.3%
1061
1.3%
951
1.3%
911
1.3%

1/29/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct37
Distinct (%)48.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.13157895
Minimum0
Maximum3554
Zeros20
Zeros (%)26.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:02.056573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4.5
Q340
95-th percentile209.75
Maximum3554
Range3554
Interquartile range (IQR)40

Descriptive statistics

Standard deviation408.7368947
Coefficient of variation (CV)5.037950697
Kurtosis72.1571219
Mean81.13157895
Median Absolute Deviation (MAD)4.5
Skewness8.398957592
Sum6166
Variance167065.8491
MonotonicityNot monotonic
2022-12-14T00:43:02.173985image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
020
26.3%
113
17.1%
44
 
5.3%
74
 
5.3%
272
 
2.6%
92
 
2.6%
1521
 
1.3%
131
 
1.3%
1301
 
1.3%
961
 
1.3%
Other values (27)27
35.5%
ValueCountFrequency (%)
020
26.3%
113
17.1%
21
 
1.3%
44
 
5.3%
51
 
1.3%
61
 
1.3%
74
 
5.3%
81
 
1.3%
92
 
2.6%
101
 
1.3%
ValueCountFrequency (%)
35541
1.3%
2961
1.3%
2771
1.3%
2211
1.3%
2061
1.3%
1521
1.3%
1471
1.3%
1301
1.3%
1111
1.3%
1091
1.3%

1/30/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct39
Distinct (%)51.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean108.3421053
Minimum0
Maximum4903
Zeros17
Zeros (%)22.4%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:02.287567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4.5
Q344.5
95-th percentile277.25
Maximum4903
Range4903
Interquartile range (IQR)43.5

Descriptive statistics

Standard deviation563.756036
Coefficient of variation (CV)5.203480536
Kurtosis72.45017206
Mean108.3421053
Median Absolute Deviation (MAD)4.5
Skewness8.423743667
Sum8234
Variance317820.8681
MonotonicityNot monotonic
2022-12-14T00:43:02.397287image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
017
22.4%
114
18.4%
44
 
5.3%
143
 
3.9%
22
 
2.6%
102
 
2.6%
82
 
2.6%
1581
 
1.3%
1121
 
1.3%
351
 
1.3%
Other values (29)29
38.2%
ValueCountFrequency (%)
017
22.4%
114
18.4%
22
 
2.6%
31
 
1.3%
44
 
5.3%
51
 
1.3%
71
 
1.3%
82
 
2.6%
91
 
1.3%
102
 
2.6%
ValueCountFrequency (%)
49031
1.3%
4281
1.3%
3541
1.3%
2781
1.3%
2771
1.3%
2001
1.3%
1821
1.3%
1621
1.3%
1581
1.3%
1421
1.3%

1/31/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct41
Distinct (%)53.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean130.6184211
Minimum0
Maximum5806
Zeros11
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:02.507601image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median6
Q353.75
95-th percentile337
Maximum5806
Range5806
Interquartile range (IQR)52.75

Descriptive statistics

Standard deviation668.0770807
Coefficient of variation (CV)5.114723294
Kurtosis72.10513188
Mean130.6184211
Median Absolute Deviation (MAD)6
Skewness8.395546556
Sum9927
Variance446326.9858
MonotonicityNot monotonic
2022-12-14T00:43:02.616645image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
115
19.7%
011
 
14.5%
27
 
9.2%
42
 
2.6%
292
 
2.6%
52
 
2.6%
872
 
2.6%
82
 
2.6%
191
 
1.3%
321
 
1.3%
Other values (31)31
40.8%
ValueCountFrequency (%)
011
14.5%
115
19.7%
27
9.2%
31
 
1.3%
42
 
2.6%
52
 
2.6%
71
 
1.3%
82
 
2.6%
101
 
1.3%
111
 
1.3%
ValueCountFrequency (%)
58061
1.3%
5381
1.3%
4361
1.3%
3521
1.3%
3321
1.3%
2401
1.3%
2371
1.3%
2111
1.3%
1841
1.3%
1771
1.3%

2/1/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct44
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.3947368
Minimum0
Maximum7153
Zeros8
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:02.730377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median7.5
Q368
95-th percentile397.25
Maximum7153
Range7153
Interquartile range (IQR)67

Descriptive statistics

Standard deviation822.4363656
Coefficient of variation (CV)5.192321298
Kurtosis72.44616371
Mean158.3947368
Median Absolute Deviation (MAD)7.5
Skewness8.423231137
Sum12038
Variance676401.5754
MonotonicityNot monotonic
2022-12-14T00:43:02.842706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
118
23.7%
08
 
10.5%
25
 
6.6%
43
 
3.9%
82
 
2.6%
62
 
2.6%
191
 
1.3%
471
 
1.3%
2071
 
1.3%
411
 
1.3%
Other values (34)34
44.7%
ValueCountFrequency (%)
08
10.5%
118
23.7%
25
 
6.6%
31
 
1.3%
43
 
3.9%
62
 
2.6%
71
 
1.3%
82
 
2.6%
91
 
1.3%
101
 
1.3%
ValueCountFrequency (%)
71531
1.3%
5991
1.3%
5351
1.3%
4221
1.3%
3891
1.3%
2971
1.3%
2861
1.3%
2471
1.3%
2071
1.3%
2061
1.3%

2/2/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct42
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.8815789
Minimum0
Maximum11177
Zeros8
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:02.961957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q376.25
95-th percentile470.5
Maximum11177
Range11177
Interquartile range (IQR)75.25

Descriptive statistics

Standard deviation1281.485991
Coefficient of variation (CV)5.801687933
Kurtosis74.04920433
Mean220.8815789
Median Absolute Deviation (MAD)8
Skewness8.5552512
Sum16787
Variance1642206.346
MonotonicityNot monotonic
2022-12-14T00:43:03.071090image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
115
19.7%
29
 
11.8%
08
 
10.5%
42
 
2.6%
62
 
2.6%
82
 
2.6%
102
 
2.6%
152
 
2.6%
211
 
1.3%
1821
 
1.3%
Other values (32)32
42.1%
ValueCountFrequency (%)
08
10.5%
115
19.7%
29
11.8%
42
 
2.6%
51
 
1.3%
62
 
2.6%
82
 
2.6%
102
 
2.6%
111
 
1.3%
152
 
2.6%
ValueCountFrequency (%)
111771
1.3%
6611
1.3%
6321
1.3%
4931
1.3%
4631
1.3%
3401
1.3%
3331
1.3%
3001
1.3%
2361
1.3%
2311
1.3%

2/3/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct42
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean261.5921053
Minimum0
Maximum13522
Zeros7
Zeros (%)9.2%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:03.185448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median8
Q383.75
95-th percentile532.25
Maximum13522
Range13522
Interquartile range (IQR)82.75

Descriptive statistics

Standard deviation1549.821299
Coefficient of variation (CV)5.924572139
Kurtosis74.28564871
Mean261.5921053
Median Absolute Deviation (MAD)8
Skewness8.574776329
Sum19881
Variance2401946.058
MonotonicityNot monotonic
2022-12-14T00:43:03.313991image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
114
18.4%
210
 
13.2%
07
 
9.2%
83
 
3.9%
742
 
2.6%
312
 
2.6%
152
 
2.6%
42
 
2.6%
181
 
1.3%
61
 
1.3%
Other values (32)32
42.1%
ValueCountFrequency (%)
07
9.2%
114
18.4%
210
13.2%
31
 
1.3%
42
 
2.6%
51
 
1.3%
61
 
1.3%
83
 
3.9%
101
 
1.3%
121
 
1.3%
ValueCountFrequency (%)
135221
1.3%
7251
1.3%
7241
1.3%
5661
1.3%
5211
1.3%
4081
1.3%
3911
1.3%
3371
1.3%
2711
1.3%
2591
1.3%

2/4/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct45
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean314.3684211
Minimum0
Maximum16678
Zeros6
Zeros (%)7.9%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:03.433423image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10
Q391.25
95-th percentile613.5
Maximum16678
Range16678
Interquartile range (IQR)90.25

Descriptive statistics

Standard deviation1911.061938
Coefficient of variation (CV)6.079051869
Kurtosis74.52090272
Mean314.3684211
Median Absolute Deviation (MAD)9.5
Skewness8.594291605
Sum23892
Variance3652157.729
MonotonicityNot monotonic
2022-12-14T00:43:03.546531image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
115
19.7%
29
 
11.8%
06
 
7.9%
42
 
2.6%
812
 
2.6%
102
 
2.6%
32
 
2.6%
121
 
1.3%
251
 
1.3%
671
 
1.3%
Other values (35)35
46.1%
ValueCountFrequency (%)
06
 
7.9%
115
19.7%
29
11.8%
32
 
2.6%
42
 
2.6%
51
 
1.3%
61
 
1.3%
81
 
1.3%
102
 
2.6%
111
 
1.3%
ValueCountFrequency (%)
166781
1.3%
8291
1.3%
8131
1.3%
6751
1.3%
5931
1.3%
4801
1.3%
4761
1.3%
3661
1.3%
3081
1.3%
2821
1.3%

2/5/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct45
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean363.6315789
Minimum0
Maximum19665
Zeros5
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:03.668510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median10.5
Q3106.25
95-th percentile686.75
Maximum19665
Range19665
Interquartile range (IQR)105.25

Descriptive statistics

Standard deviation2252.856882
Coefficient of variation (CV)6.195437943
Kurtosis74.69948658
Mean363.6315789
Median Absolute Deviation (MAD)10
Skewness8.609092857
Sum27636
Variance5075364.129
MonotonicityNot monotonic
2022-12-14T00:43:03.786079image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
115
19.7%
210
 
13.2%
05
 
6.6%
32
 
2.6%
42
 
2.6%
8952
 
2.6%
122
 
2.6%
191
 
1.3%
3011
 
1.3%
691
 
1.3%
Other values (35)35
46.1%
ValueCountFrequency (%)
05
 
6.6%
115
19.7%
210
13.2%
32
 
2.6%
42
 
2.6%
51
 
1.3%
61
 
1.3%
81
 
1.3%
101
 
1.3%
111
 
1.3%
ValueCountFrequency (%)
196651
1.3%
8952
2.6%
7641
1.3%
6611
1.3%
5481
1.3%
5301
1.3%
3891
1.3%
3411
1.3%
3071
1.3%
3011
1.3%

2/6/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct45
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean405.5
Minimum0
Maximum22112
Zeros5
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:03.911203image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median11
Q3112.75
95-th percentile746
Maximum22112
Range22112
Interquartile range (IQR)111.75

Descriptive statistics

Standard deviation2532.886156
Coefficient of variation (CV)6.246328375
Kurtosis74.78524512
Mean405.5
Median Absolute Deviation (MAD)11
Skewness8.616202983
Sum30818
Variance6415512.28
MonotonicityNot monotonic
2022-12-14T00:43:04.020844image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
115
19.7%
210
 
13.2%
05
 
6.6%
43
 
3.9%
122
 
2.6%
102
 
2.6%
451
 
1.3%
3211
 
1.3%
791
 
1.3%
361
 
1.3%
Other values (35)35
46.1%
ValueCountFrequency (%)
05
 
6.6%
115
19.7%
210
13.2%
31
 
1.3%
43
 
3.9%
51
 
1.3%
61
 
1.3%
102
 
2.6%
122
 
2.6%
161
 
1.3%
ValueCountFrequency (%)
221121
1.3%
9701
1.3%
9541
1.3%
8511
1.3%
7111
1.3%
6001
1.3%
5911
1.3%
4111
1.3%
3731
1.3%
3471
1.3%

2/7/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct42
Distinct (%)55.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean452.5263158
Minimum0
Maximum24953
Zeros4
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:04.131622image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q11.75
median12.5
Q3122.25
95-th percentile807.5
Maximum24953
Range24953
Interquartile range (IQR)120.5

Descriptive statistics

Standard deviation2857.90182
Coefficient of variation (CV)6.315437844
Kurtosis74.89509416
Mean452.5263158
Median Absolute Deviation (MAD)12
Skewness8.625310189
Sum34392
Variance8167602.813
MonotonicityNot monotonic
2022-12-14T00:43:04.249913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
115
19.7%
26
 
7.9%
34
 
5.3%
04
 
5.3%
43
 
3.9%
253
 
3.9%
102
 
2.6%
1722
 
2.6%
812
 
2.6%
2772
 
2.6%
Other values (32)33
43.4%
ValueCountFrequency (%)
04
 
5.3%
115
19.7%
26
 
7.9%
34
 
5.3%
43
 
3.9%
52
 
2.6%
61
 
1.3%
102
 
2.6%
121
 
1.3%
131
 
1.3%
ValueCountFrequency (%)
249531
1.3%
10341
1.3%
10061
1.3%
9141
1.3%
7721
1.3%
6651
1.3%
6611
1.3%
4261
1.3%
4081
1.3%
3861
1.3%

2/8/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean488.4342105
Minimum0
Maximum27100
Zeros4
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:04.364783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q11.75
median14.5
Q3127.5
95-th percentile847.5
Maximum27100
Range27100
Interquartile range (IQR)125.75

Descriptive statistics

Standard deviation3103.600392
Coefficient of variation (CV)6.354183072
Kurtosis74.95075017
Mean488.4342105
Median Absolute Deviation (MAD)13.5
Skewness8.629936656
Sum37121
Variance9632335.396
MonotonicityNot monotonic
2022-12-14T00:43:04.485347image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
115
19.7%
26
 
7.9%
04
 
5.3%
34
 
5.3%
43
 
3.9%
1952
 
2.6%
132
 
2.6%
421
 
1.3%
1381
 
1.3%
10481
 
1.3%
Other values (37)37
48.7%
ValueCountFrequency (%)
04
 
5.3%
115
19.7%
26
 
7.9%
34
 
5.3%
43
 
3.9%
51
 
1.3%
71
 
1.3%
101
 
1.3%
111
 
1.3%
132
 
2.6%
ValueCountFrequency (%)
271001
1.3%
10951
1.3%
10481
1.3%
9811
1.3%
8031
1.3%
7331
1.3%
6981
1.3%
4391
1.3%
4281
1.3%
4161
1.3%

2/9/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean528.3026316
Minimum0
Maximum29631
Zeros4
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:04.600986image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q12
median15
Q3133.5
95-th percentile886.75
Maximum29631
Range29631
Interquartile range (IQR)131.5

Descriptive statistics

Standard deviation3393.151625
Coefficient of variation (CV)6.422742235
Kurtosis75.04090552
Mean528.3026316
Median Absolute Deviation (MAD)14
Skewness8.637425682
Sum40151
Variance11513477.95
MonotonicityNot monotonic
2022-12-14T00:43:04.717138image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
114
 
18.4%
27
 
9.2%
34
 
5.3%
04
 
5.3%
43
 
3.9%
4682
 
2.6%
182
 
2.6%
452
 
2.6%
641
 
1.3%
1411
 
1.3%
Other values (36)36
47.4%
ValueCountFrequency (%)
04
 
5.3%
114
18.4%
27
9.2%
34
 
5.3%
43
 
3.9%
51
 
1.3%
71
 
1.3%
101
 
1.3%
111
 
1.3%
131
 
1.3%
ValueCountFrequency (%)
296311
1.3%
11311
1.3%
10751
1.3%
10331
1.3%
8381
1.3%
7791
1.3%
7401
1.3%
4682
2.6%
4441
1.3%
3861
1.3%

2/10/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct45
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean562.6710526
Minimum0
Maximum31728
Zeros4
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:04.834906image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.75
Q12
median16
Q3140.75
95-th percentile927.5
Maximum31728
Range31728
Interquartile range (IQR)138.75

Descriptive statistics

Standard deviation3632.923584
Coefficient of variation (CV)6.456567416
Kurtosis75.10244624
Mean562.6710526
Median Absolute Deviation (MAD)15
Skewness8.642537854
Sum42763
Variance13198133.77
MonotonicityNot monotonic
2022-12-14T00:43:04.944773image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
114
 
18.4%
27
 
9.2%
04
 
5.3%
33
 
3.9%
183
 
3.9%
43
 
3.9%
82
 
2.6%
492
 
2.6%
142
 
2.6%
261
 
1.3%
Other values (35)35
46.1%
ValueCountFrequency (%)
04
 
5.3%
114
18.4%
27
9.2%
33
 
3.9%
43
 
3.9%
51
 
1.3%
82
 
2.6%
101
 
1.3%
111
 
1.3%
142
 
2.6%
ValueCountFrequency (%)
317281
1.3%
11591
1.3%
10921
1.3%
10731
1.3%
8791
1.3%
8301
1.3%
7711
1.3%
4921
1.3%
4861
1.3%
4661
1.3%

2/11/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean589.5131579
Minimum0
Maximum33366
Zeros3
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:05.065221image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median17
Q3146.25
95-th percentile960.25
Maximum33366
Range33366
Interquartile range (IQR)144.25

Descriptive statistics

Standard deviation3820.282309
Coefficient of variation (CV)6.480402104
Kurtosis75.13954352
Mean589.5131579
Median Absolute Deviation (MAD)16
Skewness8.64562009
Sum44803
Variance14594556.92
MonotonicityNot monotonic
2022-12-14T00:43:05.188129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
115
19.7%
27
 
9.2%
03
 
3.9%
33
 
3.9%
183
 
3.9%
43
 
3.9%
82
 
2.6%
281
 
1.3%
1061
 
1.3%
551
 
1.3%
Other values (37)37
48.7%
ValueCountFrequency (%)
03
 
3.9%
115
19.7%
27
9.2%
33
 
3.9%
43
 
3.9%
51
 
1.3%
82
 
2.6%
101
 
1.3%
111
 
1.3%
151
 
1.3%
ValueCountFrequency (%)
333661
1.3%
11771
1.3%
11171
1.3%
11051
1.3%
9121
1.3%
8601
1.3%
8041
1.3%
5151
1.3%
5051
1.3%
4871
1.3%

2/12/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean595.0263158
Minimum0
Maximum33366
Zeros3
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:05.319530image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median17
Q3161.5
95-th percentile992.25
Maximum33366
Range33366
Interquartile range (IQR)159.5

Descriptive statistics

Standard deviation3820.307761
Coefficient of variation (CV)6.420401349
Kurtosis75.08483287
Mean595.0263158
Median Absolute Deviation (MAD)16
Skewness8.641047773
Sum45222
Variance14594751.39
MonotonicityNot monotonic
2022-12-14T00:43:05.437429image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
115
19.7%
27
 
9.2%
03
 
3.9%
43
 
3.9%
183
 
3.9%
33
 
3.9%
282
 
2.6%
502
 
2.6%
51
 
1.3%
111
 
1.3%
Other values (36)36
47.4%
ValueCountFrequency (%)
03
 
3.9%
115
19.7%
27
9.2%
33
 
3.9%
43
 
3.9%
51
 
1.3%
81
 
1.3%
91
 
1.3%
101
 
1.3%
111
 
1.3%
ValueCountFrequency (%)
333661
1.3%
12191
1.3%
11351
1.3%
11311
1.3%
9461
1.3%
8891
1.3%
8441
1.3%
5431
1.3%
5181
1.3%
4971
1.3%

2/13/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean794.3421053
Minimum0
Maximum48206
Zeros2
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:05.557327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median17
Q3161.5
95-th percentile1012.25
Maximum48206
Range48206
Interquartile range (IQR)159.5

Descriptive statistics

Standard deviation5519.049151
Coefficient of variation (CV)6.947949901
Kurtosis75.53975077
Mean794.3421053
Median Absolute Deviation (MAD)16
Skewness8.679069653
Sum60370
Variance30459903.53
MonotonicityNot monotonic
2022-12-14T00:43:05.678975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
115
19.7%
28
 
10.5%
33
 
3.9%
43
 
3.9%
02
 
2.6%
182
 
2.6%
162
 
2.6%
282
 
2.6%
1191
 
1.3%
631
 
1.3%
Other values (37)37
48.7%
ValueCountFrequency (%)
02
 
2.6%
115
19.7%
28
10.5%
33
 
3.9%
43
 
3.9%
51
 
1.3%
81
 
1.3%
91
 
1.3%
101
 
1.3%
111
 
1.3%
ValueCountFrequency (%)
482061
1.3%
12411
1.3%
11691
1.3%
11451
1.3%
9681
1.3%
9101
1.3%
8721
1.3%
5701
1.3%
5291
1.3%
5091
1.3%

2/14/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean880.0921053
Minimum0
Maximum54406
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:05.795494image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median17
Q3176
95-th percentile1029.75
Maximum54406
Range54406
Interquartile range (IQR)174

Descriptive statistics

Standard deviation6229.115268
Coefficient of variation (CV)7.077799279
Kurtosis75.62439546
Mean880.0921053
Median Absolute Deviation (MAD)16
Skewness8.686163384
Sum66887
Variance38801877.02
MonotonicityNot monotonic
2022-12-14T00:43:05.914041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
116
21.1%
28
 
10.5%
33
 
3.9%
43
 
3.9%
652
 
2.6%
182
 
2.6%
672
 
2.6%
162
 
2.6%
9341
 
1.3%
1621
 
1.3%
Other values (36)36
47.4%
ValueCountFrequency (%)
01
 
1.3%
116
21.1%
28
10.5%
33
 
3.9%
43
 
3.9%
51
 
1.3%
81
 
1.3%
91
 
1.3%
101
 
1.3%
111
 
1.3%
ValueCountFrequency (%)
544061
1.3%
12611
1.3%
11841
1.3%
11551
1.3%
9881
1.3%
9341
1.3%
9001
1.3%
5931
1.3%
5371
1.3%
5231
1.3%

2/15/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct46
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean908.3157895
Minimum0
Maximum56249
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:06.029790image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median17
Q3184
95-th percentile1041.25
Maximum56249
Range56249
Interquartile range (IQR)182

Descriptive statistics

Standard deviation6440.074594
Coefficient of variation (CV)7.09012732
Kurtosis75.63596334
Mean908.3157895
Median Absolute Deviation (MAD)16
Skewness8.687133194
Sum69032
Variance41474560.78
MonotonicityNot monotonic
2022-12-14T00:43:06.138877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
116
21.1%
28
 
10.5%
33
 
3.9%
43
 
3.9%
2852
 
2.6%
182
 
2.6%
702
 
2.6%
162
 
2.6%
9501
 
1.3%
1681
 
1.3%
Other values (36)36
47.4%
ValueCountFrequency (%)
01
 
1.3%
116
21.1%
28
10.5%
33
 
3.9%
43
 
3.9%
51
 
1.3%
81
 
1.3%
91
 
1.3%
101
 
1.3%
121
 
1.3%
ValueCountFrequency (%)
562491
1.3%
12941
1.3%
12121
1.3%
11621
1.3%
10011
1.3%
9501
1.3%
9131
1.3%
6041
1.3%
5441
1.3%
5321
1.3%

2/16/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean937.1842105
Minimum0
Maximum58182
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:06.259860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median17
Q3187.25
95-th percentile1044.75
Maximum58182
Range58182
Interquartile range (IQR)185.25

Descriptive statistics

Standard deviation6661.357307
Coefficient of variation (CV)7.107842014
Kurtosis75.6503292
Mean937.1842105
Median Absolute Deviation (MAD)16
Skewness8.688336686
Sum71226
Variance44373681.17
MonotonicityNot monotonic
2022-12-14T00:43:06.373042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
116
21.1%
28
 
10.5%
33
 
3.9%
43
 
3.9%
92
 
2.6%
162
 
2.6%
702
 
2.6%
9621
 
1.3%
201
 
1.3%
711
 
1.3%
Other values (37)37
48.7%
ValueCountFrequency (%)
01
 
1.3%
116
21.1%
28
10.5%
33
 
3.9%
43
 
3.9%
51
 
1.3%
92
 
2.6%
101
 
1.3%
121
 
1.3%
162
 
2.6%
ValueCountFrequency (%)
581821
1.3%
13161
1.3%
12311
1.3%
11671
1.3%
10041
1.3%
9621
1.3%
9251
1.3%
6171
1.3%
5511
1.3%
5371
1.3%

2/17/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean963.9473684
Minimum0
Maximum59989
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:06.494835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median17
Q3187.75
95-th percentile1047.25
Maximum59989
Range59989
Interquartile range (IQR)185.75

Descriptive statistics

Standard deviation6868.213134
Coefficient of variation (CV)7.12509143
Kurtosis75.66424808
Mean963.9473684
Median Absolute Deviation (MAD)16
Skewness8.689501957
Sum73260
Variance47172351.65
MonotonicityNot monotonic
2022-12-14T00:43:06.607738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
116
21.1%
28
 
10.5%
33
 
3.9%
92
 
2.6%
42
 
2.6%
52
 
2.6%
162
 
2.6%
222
 
2.6%
9731
 
1.3%
661
 
1.3%
Other values (37)37
48.7%
ValueCountFrequency (%)
01
 
1.3%
116
21.1%
28
10.5%
33
 
3.9%
42
 
2.6%
52
 
2.6%
92
 
2.6%
101
 
1.3%
121
 
1.3%
162
 
2.6%
ValueCountFrequency (%)
599891
1.3%
13221
1.3%
12461
1.3%
11711
1.3%
10061
1.3%
9731
1.3%
9301
1.3%
6261
1.3%
5531
1.3%
5411
1.3%

2/18/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct47
Distinct (%)61.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean988.6578947
Minimum0
Maximum61682
Zeros1
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:07.898028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median17
Q3189
95-th percentile1048.25
Maximum61682
Range61682
Interquartile range (IQR)187

Descriptive statistics

Standard deviation7062.05738
Coefficient of variation (CV)7.143074887
Kurtosis75.67672171
Mean988.6578947
Median Absolute Deviation (MAD)16
Skewness8.690546802
Sum75138
Variance49872654.44
MonotonicityNot monotonic
2022-12-14T00:43:08.010756image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
116
21.1%
28
 
10.5%
33
 
3.9%
92
 
2.6%
42
 
2.6%
52
 
2.6%
162
 
2.6%
222
 
2.6%
9821
 
1.3%
741
 
1.3%
Other values (37)37
48.7%
ValueCountFrequency (%)
01
 
1.3%
116
21.1%
28
10.5%
33
 
3.9%
42
 
2.6%
52
 
2.6%
92
 
2.6%
101
 
1.3%
121
 
1.3%
162
 
2.6%
ValueCountFrequency (%)
616821
1.3%
13281
1.3%
12571
1.3%
11721
1.3%
10071
1.3%
9821
1.3%
9331
1.3%
6291
1.3%
5551
1.3%
5431
1.3%

2/19/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct46
Distinct (%)60.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean995.2763158
Minimum1
Maximum62031
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:08.140051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median17
Q3189.5
95-th percentile1049.5
Maximum62031
Range62030
Interquartile range (IQR)187.5

Descriptive statistics

Standard deviation7101.914344
Coefficient of variation (CV)7.135620763
Kurtosis75.67589329
Mean995.2763158
Median Absolute Deviation (MAD)16
Skewness8.690474659
Sum75641
Variance50437187.35
MonotonicityNot monotonic
2022-12-14T00:43:08.261368image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
116
21.1%
29
 
11.8%
33
 
3.9%
842
 
2.6%
92
 
2.6%
42
 
2.6%
52
 
2.6%
162
 
2.6%
5141
 
1.3%
1301
 
1.3%
Other values (36)36
47.4%
ValueCountFrequency (%)
116
21.1%
29
11.8%
33
 
3.9%
42
 
2.6%
52
 
2.6%
92
 
2.6%
101
 
1.3%
121
 
1.3%
162
 
2.6%
181
 
1.3%
ValueCountFrequency (%)
620311
1.3%
13311
1.3%
12621
1.3%
11741
1.3%
10081
1.3%
9861
1.3%
9341
1.3%
6311
1.3%
6211
1.3%
5601
1.3%

2/20/20
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct45
Distinct (%)59.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1002.618421
Minimum1
Maximum62442
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size736.0 B
2022-12-14T00:43:08.380532image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median17
Q3191.75
95-th percentile1051.25
Maximum62442
Range62441
Interquartile range (IQR)189.75

Descriptive statistics

Standard deviation7148.806946
Coefficient of variation (CV)7.130137244
Kurtosis75.67923006
Mean1002.618421
Median Absolute Deviation (MAD)16
Skewness8.69075405
Sum76199
Variance51105440.75
MonotonicityNot monotonic
2022-12-14T00:43:08.490916image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
116
21.1%
28
 
10.5%
33
 
3.9%
53
 
3.9%
92
 
2.6%
912
 
2.6%
42
 
2.6%
2452
 
2.6%
162
 
2.6%
1041
 
1.3%
Other values (35)35
46.1%
ValueCountFrequency (%)
116
21.1%
28
10.5%
33
 
3.9%
42
 
2.6%
53
 
3.9%
92
 
2.6%
101
 
1.3%
121
 
1.3%
162
 
2.6%
181
 
1.3%
ValueCountFrequency (%)
624421
1.3%
13321
1.3%
12651
1.3%
11751
1.3%
10101
1.3%
9871
1.3%
9341
1.3%
6341
1.3%
6311
1.3%
5671
1.3%

Interactions

2022-12-14T00:42:55.792795image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:21.030336image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:24.125730image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:27.184646image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:30.107926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:33.109886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:36.084520image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:39.372878image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:42.318615image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:45.630014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:48.759491image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:51.715147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:54.889052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:57.800813image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:00.732734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:04.007552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:06.947933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:09.967743image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:13.458534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:17.116976image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:20.124283image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:23.067102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:25.876819image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:28.749018image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:32.356439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:35.307444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:38.106147image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:40.841229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:43.727966image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:47.445961image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:50.240867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:53.015363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:55.875061image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:21.147337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:24.219386image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:27.270124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:30.193174image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:33.198860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:36.172605image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:39.458509image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:42.407812image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:45.720582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:48.851479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:51.795861image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:54.972268image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:57.885500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:00.815889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:04.098015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:07.040029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:10.065363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:13.542703image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:17.204235image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:20.231907image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:23.150211image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:25.960833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:29.549256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:32.443517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:35.400179image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:38.190246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:40.924014image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:43.811415image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:47.531592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:50.329123image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:53.097785image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:55.959967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:21.235972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:24.439777image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:27.353395image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:30.279323image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:33.284877image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:36.265069image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:39.545215image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:42.504483image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:45.808854image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:48.942406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:51.879686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:55.059772image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:57.973448image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:00.904454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:04.191970image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:07.129753image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:10.154718image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:13.628967image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:17.292745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:20.327325image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:23.239749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:26.044673image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:29.647119image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:32.529809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:35.481662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:38.277453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:41.006612image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:43.897201image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:47.614867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:50.411737image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:53.183656image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:56.040353image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:21.317281image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:24.523194image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:27.434128image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:30.357975image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:33.365810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:36.350796image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:39.626570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:42.598072image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:45.898825image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:49.024934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:51.963776image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:55.142467image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:58.055828image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:00.985437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:04.275534image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:07.215719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:10.243427image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:13.719066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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2022-12-14T00:42:13.040036image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:16.758662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:19.727038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:22.726846image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:25.521607image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:28.391758image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:31.994759image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:34.894377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:37.759144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:40.496914image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:43.346038image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:46.175102image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:49.899188image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:52.661913image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:55.442306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:58.308675image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:23.826456image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:26.905972image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:29.843172image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:32.845512image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:35.772569image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:39.062619image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:42.042936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:45.359840image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:48.449552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:51.442106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:54.624212image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:57.532214image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:00.454442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:03.732757image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:06.672425image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:09.662165image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:13.138499image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:16.841308image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:19.825593image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:22.810769image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:25.608525image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:28.479546image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:32.086686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:35.000296image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:37.845097image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:40.580446image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:43.442458image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:46.263088image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:49.983273image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:52.749075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:55.527568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:58.397453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:23.920438image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:27.001307image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:29.931888image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:32.933969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:35.869003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:39.173677image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:42.133881image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:45.449735image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:48.558811image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:51.533579image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:54.714442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:57.621225image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:00.546664image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:03.821606image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:06.761568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:09.759185image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:13.252266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:16.935029image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:19.923349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:22.895760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:25.699266image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:28.566498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:32.177690image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:35.103983image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:37.931042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:40.666153image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:43.538105image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:47.251903image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:50.069686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:52.839946image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:55.615378image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:58.490402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:24.025487image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:27.092933image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:30.021410image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:33.022503image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:35.971256image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:39.277734image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:42.226437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:45.541247image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:48.663810image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:51.627141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:54.798495image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:41:57.709384image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:00.637571image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:03.910714image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:06.854957image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:09.865198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:13.357928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:17.028005image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:20.025230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:22.982277image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:25.787978image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:28.656998image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:32.266926image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:35.204349image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:38.020309image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:40.754313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:43.631181image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:47.349377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:50.154141image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:52.929118image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-12-14T00:42:55.704419image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Correlations

2022-12-14T00:43:08.646462image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-14T00:43:08.929779image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-14T00:43:09.188793image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-14T00:43:09.400206image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-14T00:43:09.534902image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-14T00:42:58.707337image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-14T00:42:59.450176image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-12-14T00:42:59.692028image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/201/31/202/1/202/2/202/3/202/4/202/5/202/6/202/7/202/8/202/9/202/10/202/11/202/12/202/13/202/14/202/15/202/16/202/17/202/18/202/19/202/20/20
0AnhuiMainland China31.82571117.22641915396070106152200237297340408480530591665733779830860889910934950962973982986987
1BeijingMainland China40.18238116.414214223641688091111114139168191212228253274297315326337342352366372375380381387393395
2ChongqingMainland China30.05718107.874069275775110132147182211247300337366389411426428468486505518529537544551553555560567
3FujianMainland China26.07783117.989515101835598084101120144159179194205215224239250261267272279281285287290292293293
4GansuMainland China36.06110103.83430224714192426294051555762626779838386879090909091919191
5GuangdongMainland China23.33841113.42202632537811115120727735443653563272581389597010341095113111591177121912411261129413161322132813311332
6GuangxiMainland China23.82908108.7881252323364651587887100111127139150168172183195210215222222226235237238242244245
7GuizhouMainland China26.81536106.8748133457991229293846586471818999109127133135140143144146146146146
8HainanMainland China19.19673109.7455458192233404346526264728099106117124131138144157157159162162163163168168
9HebeiMainland China38.04280114.5149112813183348658296104113126135157172195206218239251265283291300301306306307

Last rows

Province/StateCountry/RegionLatLong1/22/201/23/201/24/201/25/201/26/201/27/201/28/201/29/201/30/201/31/202/1/202/2/202/3/202/4/202/5/202/6/202/7/202/8/202/9/202/10/202/11/202/12/202/13/202/14/202/15/202/16/202/17/202/18/202/19/202/20/20
66South AustraliaAustralia-34.9285138.6007000000000012222222222222222222
67Boston, MAUS42.3601-71.0589000000000011111111111111111111
68San Benito, CAUS36.5761-120.9876000000000000222222222222222222
69NaNBelgium50.50394.4699000000000000011111111111111111
70Madison, WIUS43.0731-89.4012000000000000001111111111111111
71Diamond Princess cruise shipOthers35.4437139.63800000000000000000616164135135175175218285355454542621634
72San Diego County, CAUS32.7157-117.1611000000000000000000001122222222
73San Antonio, TXUS29.4241-98.4936000000000000000000000011111111
74NaNEgypt26.820630.8025000000000000000000000001111111
75NaNIran32.427953.6880000000000000000000000000000025